Discussion And Conclusion

Genomics has transformed the world. Or, rather, it has altered the intellectual landscape of the biosciences: Its implications suggest that we should be able to gain access to information about biological function at a rate and on a scale previously inconceivable. Of course, our hopes and expectations remain unfulfilled. Like Watson and Crick's 1953 structure of DNA, the complete sequencing of the human genome has simply suggested more questions than it answers: It is the beginning not the end. What we can conceive of still far exceeds what can actually be done. Experimental science is playing catch-up, developing postgenomic strategies that can exploit the information explosion implicit within genomics. Biology remains at risk of being overwhelmed by the deluge of new data on a hitherto unknown scale and complexity. The trick is to pull out the useful and discard the worthless, yielding first knowledge and then true understanding and the ability to efficiently manipulate biological systems.

One of the tasks of modern drug research is to evaluate this embarrassment of riches. Can we reduce incoherent data into usable and comprehensible information? Can we extract knowledge from this information? How much useful data is locked away in the literature? Can we ultimately draw out understanding from the accumulation of knowledge? One way that we can attack this problem is through computer-based informatics techniques, including bioinformatics. This is not meant, of course, to replace human involvement in the process. It is merely a powerful supplement compensating for an area where the human mind is relatively weak: the fast, accurate processing of huge data sets. Bioinformatics has already made significant contributions to drug discovery and has begun to do the same for vaccines.

Bioinformatics requires people. It always has, and probably always will. To expect informatics to behave differently from experimental science is, at best, hopeful and overly optimistic and, at worse, naive or disingenuous. Experimental science is becoming ever more reliant on instrumental analysis and robotics, yet people are still required to troubleshoot and to make sense of the results. Much the same holds for bioinformatics: We can devolve work that is routine to automation—scanning genomes, etc.—but people are still needed to ensure such automation works and to assess the results. New methods need to be developed and their results used and applied. There is only so much that putting tools on the desktops of experimentalists can achieve, useful though this is in both a tactical and a strategic sense. Annotation and reannotation is, and should be, a never-ending occupation. For that which we automate, sensible and useful ontologies still need to be built and verified. The dynamic interplay between people and algorithms remains at the heart of bioinformatics. Long may it be so: That's what makes it fun.

Academic bioinformaticians often forget their place as an intermediate taking, interpreting, and ultimately returning data from one experimental scientist to another. There is a need for bioinformatics to keep in close touch with wet laboratory biologists, servicing and supporting their needs, either directly or indirectly, rather than becoming obsessed with their own recondite or self-referential concerns. Moreover, it is important to realize, and reflect upon, our own shortcomings. Central to the quest to achieve automated gene elucidation and characterization are pivotal concepts regarding the manifestation of protein function and the nature of sequence-structure and sequence-function relations. The use of computers to model these concepts is limited by our currently limited understanding, in a physicochemical rather than phenomenological sense, of even simple biological processes. Understanding and accepting what cannot be done informs our appreciation of what can be done. In the absence of such an understanding, it is easy to be misled, as specious arguments are used to promulgate overenthusiastic notions of what particular methods can achieve. The road ahead must be paved with caution and pragmatism. The future belongs, or should belong, to those scientists who are able to master both computational and experimental disciplines.

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